System and method of identifying sources for biological rhythms

ABSTRACT

A system and method of locating a source of a heart rhythm disorder are provided in which a first pair of cardiac signals is processed to define a first coefficient associated with variability of the first pair of signals at a first region of the heart. A second pair of cardiac signals is processed to define a second coefficient associated with variability of the second pair of signals at a second region of the heart. Thereafter, the first coefficient of variability is compared to the second coefficient of variability to determine a direction towards the source of the rhythm disorder.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/559,868, filed Jul. 27, 2012, now issued as U.S. Pat. No. 9,408,536,which claims the benefit of U.S. Provisional Application No. 61/569,132,filed Dec. 9, 2011, the disclosure of which is incorporated herein byreference in its entirety.

GOVERNMENT RIGHTS

This invention was made with government support under Grants HL83359 andHL103800 awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

FIELD OF THE INVENTION

The present application relates generally to biological rhythmdisorders. More specifically, the present application is directed to asystem and method of identifying a source (or sources) of a biologicalrhythm disorder, such as a heart rhythm disorder.

BACKGROUND OF THE INVENTION

Heart rhythm disorders are common and represent significant causes ofmorbidity and death throughout the world. Malfunction of the electricalsystem in the heart represents a proximate cause of heart rhythmdisorders. Heart rhythm disorders exist in many forms, of which the mostcomplex and difficult to treat are atrial fibrillation (AF), ventriculartachycardia (VT) and ventricular fibrillation (VF). Other rhythmdisorders are more simple to treat, but may also be clinicallysignificant including atrial tachycardia (AT), supraventriculartachycardia (SVT), atrial flutter (AFL), premature atrialcomplexes/beats (SVE) and premature ventricular complexes/beats (PVC).While under normal conditions the sinus node keeps the heart in sinusrhythm, under certain conditions rapid activation of the normal sinusnode can cause inappropriate sinus tachycardia or sinus node reentry,both of which also represent heart rhythm disorders.

Treatment of heart rhythm disorders—particularly complex rhythmdisorders of AF, VF and VT—can be very difficult. Pharmacologic therapyfor complex rhythm disorder is not optimal. Ablation has been usedincreasingly in connection with heart rhythm disorders by maneuvering asensor/probe to the heart through the blood vessels, or directly atsurgery, and delivering energy to a location of the heart to mitigateand in some cases to eliminate the heart rhythm disorder. However, incomplex rhythm disorders ablation is often difficult and ineffectualbecause tools that identify and locate a cause (source) of the heartrhythm disorder are poor and hinder attempts to deliver energy to acorrect region of the heart to eliminate the disorder.

Certain systems and methods are known for treating simple heart rhythmdisorders. In a simple heart rhythm disorder (e.g., atrial tachycardia),the source of the disorder can be identified by tracing activation backto the earliest location, which can be ablated to mitigate and in somecases to eliminate the disorder. However, even in simple heart rhythmdisorders, ablating the cause of a heart rhythm disorder is challengingand experienced practitioners often require hours to ablate simplerhythm disorders that show consistent beat-to-beat activation patterns,such as atrial tachycardia.

There are few, if any, known systems and methods that have beensuccessful with respect to identifying the sources or causes for complexrhythm disorders such as AF, VF or polymorphic VT. In a complex rhythmdisorder, an earliest location of activation onsets cannot be identifiedbecause activation onset patterns change from beat to beat and are oftencontinuous without an earliest or a latest point.

Diagnosing and treating heart rhythm disorders generally involves theintroduction of a catheter having a plurality of sensors/probes into theheart through blood vessels of a patient. The sensors detect electricactivity of the heart at sensor locations in the heart. The electricactivity is generally processed into electrogram signals that representthe activation of the heart at the sensor locations.

In a simple heart rhythm disorder, the signal at each sensor location isgenerally consistent from beat to beat, enabling identification of theearliest activation. However, in a complex rhythm disorder, the signalat each sensor location from beat to beat may transition between one,several, and multiple deflections of various shapes. For instance, whena signal for a sensor location in AF includes 5, 7, 11 or moredeflections, it is difficult if not impossible to identify whichdeflections in the signal are local to the sensor location in the heart(i.e., local activation onset) versus a nearby sensor location in theheart (i.e., far-field activation onset) or simply noise from anotherpart of the patient's heart, other anatomic structures or externalelectronic systems. The foregoing deflections make it difficult if notimpossible to identify activation onset times of the beats in a signalat a sensor location.

Current strategies in complex rhythm disorders have also consideredregularity in signals at sensor locations as a surrogate for the sourceof the complex rhythm disorder, i.e., the source being more organized atcertain sensor locations than at adjacent sensor locations. For example,U.S. Pat. No. 7,117,030 by Berenfeld et al. and U.S. Pat. No. 5,792,189by Gray et al. exemplify the current approaches in which the source(s)for variable atrial fibrillation (AF) are considered highly regular.However, these approaches have indeed been disappointing in finding thesource to treat human atrial fibrillation. As another example, Sanderset al. (Circulation 2005) found that locations of regularity, indicatedby high spectral dominant frequency with a high regularity index, wererarely locations where AF terminated by ablation in complex (persistent)AF. Other studies such as Sahadevan (Circulation 2004) identifiedlocations of rapid regular activity in human AF that have never beenshown to drive human AF. Animal models (Kalifa, Circulation 2006) andhuman studies (Nademanee, J Am Coll 2004) suggest that complexfractionated atrial electrograms (CFAE) may form at the junction fromregular ‘drivers’ to variable AF. In clinical use, however, such CFAEsites are poor targets for AF treatment (Oral, Circulation 2007).

There are no known systems and methods that have been able to identifythe source (or sources) for a heart rhythm disorder independently ofidentifying and assigning activation onset times to signals ofsuccessive beats. Given the difficulties in identifying the activationonset times, this has significantly limited diagnosis of the source (orsources) of heart rhythm disorders, especially for complex rhythmdisorders, and has limited treatment attempts at their elimination.

SUMMARY

The present invention is applicable to identifying sources of variousrhythms, including normal and disordered heart rhythms, as well as otherbiological rhythms and rhythm disorders, such as neurological seizures,esophageal spasms, bladder instability, irritable bowel syndrome, andother biological disorders for which biological signals can be recordedto permit determination, diagnosis, and/or treatment of the cause (orsource) of the disorders. The invention does not rely on or calculatethe onset of activation in signals at any sensor locations, and thus itis particularly useful in complex rhythm disorders which provide complexactivation patterns and complex varying beat signals. It is especiallyuseful in identifying the cause(s) of the disorders of heart rhythm suchthat they can be treated with expediency.

Complex heart rhythm disorders typically result in an array ofactivation patterns that are extremely difficult to decipher, so thatthe ability to determine accurate activation of a heart beat haspreviously not been possible. Among the advantages of the presentinvention is the ability to identify a source of a complex rhythmdisorder from variability in signals at sensor locations relative toregularity in signals at adjacent sensor locations, independently of theassignment of specific activation onset times (identifying beats) insignals at these sensor locations. In this way, the invention enables adetermination of a source (or sources) of the heart rhythm disorder fortreatment. Another advantage is that the present invention provides amethod and system which can be carried out rapidly while a sensingdevice—such as a catheter having sensors thereon—is used in or near thepatient and is followed by treatment of cardiac tissue to ameliorate thedisorder and in many cases to cure the disorder. Treatment may thusoccur immediately, since the invention will provide the location(s) ofthe source of the heart rhythm disorder.

Prior methods and systems suffered from the inability to determine thesource of rhythm disorders and consequently provided no means oftargeting the source for meaningful and curative treatment.Additionally, prior methods and systems required numerous and complexsteps of treatment and yet still failed to provide a means ofdetermining the source(s) of heart rhythm disorders. In contrast, thepresent invention provides a relatively few number of steps to determinethe source(s) for a heart rhythm disorder, including complex rhythmdisorders of atrial and ventricular fibrillation.

In accordance with an embodiment, a method of locating a source of arhythm disorder of a heart is disclosed. In accordance with the method,a first pair of cardiac signals is processed to define a firstcoefficient associated with variability of the first pair of signals ata first region of the heart. Further, a second pair of cardiac signalsis processed to define a second coefficient associated with variabilityof the second pair of signals at a second region of the heart.Thereafter, the first coefficient of variability is compared to thesecond coefficient of variability to determine a direction towards thesource of the rhythm disorder.

In accordance with another embodiment, a method of locating a source ofa rhythm disorder of a heart is disclosed. The method includesprocessing a first cardiac signal at one or more first time pointsagainst a second cardiac signal at one or more second time points todefine a first coefficient associated with variability of one or morecoordinate pairs of the first cardiac signal against the second cardiacsignal. The method further includes processing a third cardiac signal atone or more third time points against a fourth cardiac signal at one ormore fourth time points to define a second coefficient associated withvariability of one or more coordinate pairs of the third cardiac signalagainst the fourth cardiac signal. Thereafter, a direction towards thesource of the rhythm disorder is determined as being from a lowercoefficient of variability to a higher coefficient of variability.

In accordance with a further embodiment, a system to locate a source ofa rhythm disorder of a heart is disclosed. The system includes at leastone computing device that is configured to process a first pair ofcardiac signals to define a first coefficient associated withvariability of the first pair of signals at a first region of the heart,process a second pair of cardiac signals to define a second coefficientassociated with variability of the second pair of signals at a secondregion of the heart, and compare the first coefficient of variability tothe second coefficient of variability to determine a direction towardsthe source of the rhythm disorder.

In accordance with yet another embodiment, a system to locate a sourceof a rhythm disorder of a heart is disclosed. The system includes atleast one computing device configured to process a first cardiac signalat one or more first time points against a second cardiac signal at oneor more second time points to define a first coefficient associated withvariability of one or more coordinate pairs of the first cardiac signalagainst the second cardiac signal, process a third cardiac signal at oneor more third time points against a fourth cardiac signal at one or morefourth time points to define a second coefficient associated withvariability of one or more coordinate pairs of the third cardiac signalagainst the fourth cardiac signal, and determine a direction towards thesource of the rhythm disorder being from a lower coefficient ofvariability to a higher coefficient of variability.

In accordance with another embodiment, a method of treating a cardiacrhythm disorder is disclosed. In accordance with the method, pairs ofcardiac signals are iteratively selected from a plurality of cardiacsignals. Each pair has a first cardiac signal and different secondcardiac signal. The first cardiac signal at a plurality of first timepoints is processed against the different second cardiac signal at aplurality of second time points to define a plurality of coordinatepairs of the first cardiac signal against the different second cardiacsignal for each selected pair. A coefficient of variability that exceedsa threshold is determined. The coefficient of variability can becomputed from the plurality of coordinate pairs among the first cardiacsignal and the second cardiac signal each selected pair.

Thereafter, a matrix of coefficients of variability is constructed foreach selected pair. The matrixes of coefficients are organized for theiteratively selected pairs in relation to each other. One or moresources of the cardiac rhythm disorder are located using the organizedmatrixes of coefficients. Treatment is delivered to cardiac tissue atthe one or more sources to suppress or eliminate the cardiac rhythmdisorder.

These and other purposes, goals and advantages of the presentapplication will become apparent from the following detailed descriptionof example embodiments read in connection with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1 illustrates an example system to identify a source (or sources)of a heart rhythm disorder;

FIG. 2 illustrates an example catheter that can be used to identify thesource of a heart rhythm disorder in FIG. 1;

FIGS. 3A-B illustrates an example simple electrogram signal of a heartrhythm disorder and a complex electrogram signal of a heart rhythmdisorder from sensors positioned at sensor locations in a heartillustrated in FIG. 1;

FIG. 4A illustrates an example method of identifying a source of a heartrhythm disorder as being in a direction from regular activity (lowvariability) in signals of certain sensor locations toward irregularactivity (high variability) in signals of adjacent sensor locations inthe heart illustrated in FIG. 1;

FIGS. 4B-4F illustrate an example method of identifying a source of aheart rhythm disorder as being in a direction from regular activity (lowvariability) in signals of certain sensor locations toward irregularactivity (high variability) in signals of adjacent sensor locations inthe heart illustrated in FIG. 1;

FIG. 5 is a flowchart that illustrates an example method of determiningperiodic repeating activity and/or variability to identify a source of aheart rhythm disorder;

FIGS. 6A-6E illustrate an example migration of a source (locus) of aheart rhythm disorder and the use of coefficients of variability (orindexes of regularity) to identify such a source of heart rhythmdisorder;

FIG. 7 illustrates an example plot of indexes of periodic repeatingactivity (regularity) to identify a source of heart rhythm disorder; and

FIG. 8 is a block diagram of an illustrative embodiment of a generalcomputer system.

DETAILED DESCRIPTION

A system and method for identifying the sources of heart rhythmdisorders are disclosed herein. In the following description, for thepurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of example embodiments. Itwill be evident, however, to one skilled in the art, that an exampleembodiment may be practiced without all of the disclosed specificdetails.

FIG. 1 illustrates an example system 100 to identify a source (orsources) of a heart rhythm disorder. Specifically, the example system100 is configured to detect cardiac information (signals)collected/detected from a patient's heart in connection with a heartrhythm disorder. The system 100 is further configured to process thesignals in order to determine a region (or multiple regions) of tissuein the patient's heart associated with a degree of regularity (e.g.,lower regularity), which differs from a degree of regularity (e.g.,higher regularity) of a plurality of adjacent regions of tissue in thepatient's heart. For example, the region (or multiple regions)determined to have high variability (low regularity) surrounded by lowvariability (high regularity) indicates a source(s) of the heart rhythmdisorder. The heart includes a right atrium 122, left atrium 124, rightventricle 126 and left ventricle 128.

The example system 100 includes a catheter 102, signal processing device114, computing device 116 and analysis database 118.

The catheter 102 is configured to detect cardiac activation informationin the heart and to transmit the detected cardiac activation informationto the signal processing device 114, via a wireless connection, wiredconnection, or a combination of both wired and wireless connections. Thecatheter includes a plurality of probes/sensors 104-112, which can beinserted into the heart through the patient's blood vessels.

In some embodiments, one or more of the sensors 104-112 may not beinserted into the patient's heart. For example, some sensors may detectcardiac activation via the patient's surface (e.g.,electrocardiogram—ECG) or remotely without contact with the patient(e.g., magnetocardiogram). As another example, some sensors may alsoderive cardiac activation information from cardiac motion of anon-electrical sensing device (e.g., echocardiogram). In variousembodiments, these sensors can be used separately or in differentcombinations, and further these separate or different combinations canalso be used in combination with sensors inserted into the patient'sheart.

The sensors 104-112, which are positioned at sensor locations in theheart under consideration, can detect cardiac activation information atthe sensor locations and can further deliver energy to ablate the heartat the sensor locations. It is noted that the sensors 104-112 can alsodetect cardiac activation information from overlapping regions of theheart (e.g., right atrium 122 and left atrium 124).

The signal processing device 114 is configured to process (e.g., clarifyand amplify) the cardiac activation information detected by the sensors104-112 at the sensor locations into electrogram signals and to providethe processed signals to the computing device 116 for analysis inaccordance with methods disclosed herein. In processing the cardiacactivation information from the sensors 104-112, the signal processingdevice 114 can subtract cardiac activation information from overlappingregions of the heart 120 to provide processed signals to the computingdevice 116 for analysis. While in some embodiments, the signalprocessing device 114 is configured to provide unipolar signals, inother embodiments, the signal processing device 114 can provide bipolarsignals.

The computing device 116 is configured to receive detected/processedsignals from the signal processing device 114 and further configured toanalyze the signals in accordance with methods disclosed herein todetermine degree of regularity (or degree variability) in adjacentregions of the patient's heart, such that it is possible to generate amap(s) (representation(s)) of regularity (or variability) of theadjacent regions that can be used to locate a source(s) of the heartrhythm disorder and to eliminate the source(s).

The analysis database 118 is configured to support or aid in theanalysis of the signals by the computing device 116. In someembodiments, the analysis database 118 can store the map of regularityassociated with or generated on the basis of signals at a plurality ofadjacent sensor locations over a period of time, as will be described ingreater detail herein. The analysis database 118 can also providestorage of intermediate data associated with the map of regularity.

FIG. 2 illustrates an example catheter 200 to detect electrical signalsvia a plurality of sensors 240 at sensor locations in the heart 120under consideration. Catheter 200 can be similar to or different thancatheter 102 and sensors 240 can be similar to or different than sensors104-112 in FIG. 1.

The catheter 200 includes multiple splines (or meridians) 220 each ofwhich can include multiple sensors (or probes) 240. By rotating along ashaft axis 245, the splines or meridians 220 may be spaced or separatedmore widely spatially as depicted at 230 or spaced more closelyspatially as depicted at 235.

Different spatial arrangements of the sensors 240 (via spatialseparation of the splines 220) can have the effect of spatiallyextending the area of the heart 120 under consideration. The sensors 240positioned in a spatial arrangement at sensor locations of the heart 120under consideration can detect cardiac electrical signals at the sensorlocations and can further deliver energy to ablate (or other treatmentto treat) the heart at the sensor locations.

Different catheters with various spatial arrangements of the sensors 240can be used, such as spiral, radial spokes or other spatialarrangements.

FIG. 3A illustrates an example of simple electrocardiogram signals of aheart rhythm disorder from sensors positioned at sensor locations in theheart 120.

As an example, the heart rhythm disorder can be a complex rhythmdisorder AF, VF and polymorphic VT, or another heart rhythm disorder. Inthis example, the signals generally show identifiable activation onsets(e.g., for heart beats). The heart beats can be characterized by anactivation onset with a sharp inflection point and high sloperepresenting depolarization, followed by a period of gentle,low-deviation slope representing repolarization, typically lastingbetween about 100 ms and 250 ms.

The regularity or phase relationship between the simple signals in FIG.3A is generally easily identifiable.

FIG. 3B illustrates an example of complex electrocardiogram signals of aheart rhythm disorder from sensors positioned at sensor locations in theheart 120. As an example, the heart rhythm disorder can be a complexrhythm disorder AF, VF and polymorphic VT, or another heart rhythmdisorder.

The signals in FIG. 3B do not generally show identifiable activationonsets (e.g., for heart beats). The signals include multiple deflectionsof short duration caused by the heart rhythm disorder that makes thediscernment of activation onsets (depolarization) prohibitivelydifficult. Similarly, regularity or phase relationship between complexsignals in FIG. 3B is not easily discerned.

FIG. 4A illustrates an example method of identifying a source of a heartrhythm disorder as being in a direction from regular activity (lowvariability) in signals of certain sensor locations toward irregularactivity (high variability) in signals of adjacent sensor locations inthe heart 120 illustrated in FIG. 1.

Panel 400 illustrates a plurality of example signals (e.g., ECG signals)obtained from adjacent sensor locations (e.g., different regions) in theheart 120 of FIG. 1, such as via electrodes in the catheter 102, 200. Toillustrate the example method, a pair of signals (e.g., two (2) examplesignals) is selected from sensor locations, denoted as SIG. 1 and SIG 2.It is noted that multiple different signals can be considered from thecatheter 102, 200, e.g., 64, 128, or another number of signals. Each ofthe signals in the pair is a voltage time series. The signals havevarying amplitudes (e.g., voltage) along the signals as detected by thesensors of the catheter 102, 200. A multiplicity of time points alongeach of the example signals SIG. 1, SIG. 2 can be identified or selectedin accordance with the example methods disclosed herein.

As shown in panel 410, one or more time points are selected in SIG. 1and corresponding (e.g., contemporaneous) one or more time points areselected in SIG. 2. These corresponding time points in SIG. 1, SIG. 2are grouped (e.g., paired) into one or more coordinate pairs of thissignal pair. For example, panel 420 shows graphically such grouping ofthe time points among signal pair SIG. 1, SIG. 2. A coefficient ofvariability is defined or determined for the one or more coordinatepairs. In one example shown in panel 430, the coefficient of variabilityfor these coordinate pairs in the signal pair can be defined from atransformation matrix M that transforms the time points of SIG. 1 totime points of SIG. 2. For example, the values in the matrix M canaveraged to define the coefficient of variability. Other evaluationtechniques can be used, such as standard deviation analysis, frequencyanalysis, entropy analysis, cross-correlation analysis, randomnessanalysis, Monte Carlo simulation methods, quantification of chaos and/orother complex statistical analyses, as well as combinations thereof.

The processing described in panels 400-430 can be repeated for differentpairs of signals shown in panel 400. It is reiterated that multipledifferent signals can be considered from the catheter 102, 200, e.g.,64, 128, or another number of signals. For example, one or more timepoints can be selected in a SIG. 3 (not shown) and corresponding (e.g.,contemporaneous) one or more time points are selected in a SIG. 4 (notshown), which can be a second signal pair SIG. 3, SIG. 4. In someembodiments, SIG. 3 can be SIG. 1, e.g., a common signal among the firstpair of signals and the second pair of signals. In other embodiments,both signals can be different among the different signal pairs. Thecorresponding time points in SIG. 3, SIG. 4 are grouped (e.g., paired)into one or more coordinate pairs. A coefficient of variability isdefined or determined for the one or more coordinate pairs of the secondsignal pair SIG. 3, SIG. 4, similarly or differently that describedabove in the first signal pair SIG 1, SIG 2.

Thereafter, the source of the disorder can be determined as being in thedirection from the lower coefficients of variability (regular) towardthe higher coefficients of variability (irregular) among the differentsignal pairs processed. For example, as shown in panel 440, a firstsignal pair can have a low coefficient of variability. In other words,signals in the first signal pair can be regular, e.g., not disorganizedin amplitude (voltage) and time. As shown in panel 450, a second signalpair can have a high coefficient of variability. In other words, signalsin the second signal pair can be irregular or of higher variability thanthe signals in the first signal pair, e.g., disorganized in amplitude(voltage) and/or time. Accordingly, the source of complex rhythmdisorder lies in the direction of higher variability.

As another example, as shown in panel 460, a third signal pair can havea low coefficient of variability. In other words, signals in the thirdsignal pair can be regular, e.g., not disorganized in amplitude(voltage) and time. As shown in panel 470, a fourth signal pair can havea high coefficient of variability. In other words, signals in the fourthsignal pair can be irregular or of higher variability than the signalsin the third signal pair, e.g., disorganized in amplitude (voltage)and/or time (offset). Accordingly, the source of complex rhythm disorderlies in the direction of higher variability. For example, the highestvariability among the different signal pairs of panel 400 would berepresented by a maximum disorganization (variability) in amplitude anda maximum disorganization (variability) in delay.

FIG. 4B illustrates an example method of identifying a source of a heartrhythm disorder as being in a direction from regular activity (lowvariability) in signals of certain sensor locations toward irregularactivity (high variability) in signals of adjacent sensor locations inthe heart 120 illustrated in FIG. 1.

Panel (A) illustrates three (3) example signals (e.g., ECG signals)obtained from three adjacent sensor locations (sites 1, 2 and 3) in theheart 120 of FIG. 1, such as via electrodes in the catheter 102, 200.For the following processing example, signals from sites 1 and 2 can beconsidered a first signal pair, while sites 2 and 3 can be considered asecond signal pair, where the signal from site 2 is common among signalpairs. It is reiterated that multiple signals can be considered from thecatheter 102, 200, e.g., 64, 128, or another number of signals. Each ofthe signals is a voltage time series. The three signals have varyingamplitudes (e.g., voltage) along the signals as detected by the sensorsof the catheter 102, 200. Four example time points (A, B, C and D) areillustrated in the signals for clarity and brevity in describing theprocessing of the signals in accordance with the example method asdescribed below. However, it is to be noted that there is a multiplicityof time points along each of the example signals that can be processedin accordance with the example methods disclosed herein.

In accordance with the example method, a derivative of each signal canbe determined at the plurality of selected time points. The derivativecan be a zero order derivative or a higher-order derivative (e.g., afirst-order derivative or second-order derivative). For example, aderivative of the first (analysis) cardiac signal determined at aplurality of first time points (e.g., A, B, C and D). As anotherexample, a derivative of the second (reference) cardiac signal can bedetermined at a plurality of second time points (e.g., A, B, C and D).Similarly, a derivative of the third cardiac signal is determined at aplurality of third time points (e.g., A, B, C and D). In someembodiments, the pluralities of time points in the different signals arecontemporaneous. It is again noted that the signals include amultiplicity of time points that can be processed in accordance with theexample methods as described herein.

Referring to FIG. 4C, the derivative of the first (analysis) cardiacsignal at the plurality of first time points can be processed againstthe derivative of the second (reference) cardiac signal at the pluralityof second time points to define a plurality of coordinate pairs of thefirst cardiac signal against the second cardiac signal in the firstsignal pair. These coordinate pairs can be maintained in memory and/orsaved to database 118. In some embodiments, the plurality of coordinatepairs associated with processing of the first cardiac signal against thesecond cardiac signal can be plotted and connected to generate aplurality of loops. For example, the coordinate pairs associated withexample time points A-D can be plotted and connected to generate a firstloop, as shown in a left part of FIG. 4C.

The plotting and connecting can be repeated for a plurality of first andsecond time points to generate multiple loops as shown in a right partof FIG. 4C. In this example, a single loop is shown in a left part ofFIG. 4C based on time points A-D for illustrative purposes. The singleloop can represent a single cycle of a heart rhythm, while multipleloops can represent multiple cycles of the heart rhythm. As illustratedin the right part of FIG. 4C, a high degree of regularity (e.g., lowdegree of variability) is observed among the loops in the right part ofFIG. 4C. It is noted that the same processing can be repeated for thefirst (analysis) cardiac signal against different second (reference)cardiac signals, i.e., others of the adjacent 64 or 128 signals.

In FIG. 4D, the derivative of the second (analysis) cardiac signal atthe plurality of first time points in FIG. 4B can be processed againstthe derivative of the third (reference) cardiac signal at the pluralityof third time points to define a plurality of coordinate pairs of thesecond cardiac signal against the third cardiac signal in the secondsignal pair. These coordinate pairs can be maintained in memory and/orsaved to database 118. In some embodiments, the plurality of coordinatepairs associated with processing of the second cardiac signal againstthe third cardiac signal can be plotted and connected to generate aplurality of loops. It is noted that the same processing can be repeatedfor the second (analysis) cardiac signal against different third(reference) cardiac signals, i.e., others of the adjacent 64 or 128signals.

Further with reference to FIG. 4D, the plotting and connecting can berepeated for a plurality of second and third time points to generatemultiple loops as shown in a right part of FIG. 4D. In this example, asingle loop is shown in a left part of FIG. 4D based on time points A-Dfor illustrative purposes. The single loop can represent a single cycleof a heart rhythm, while multiple loops can represent multiple cycles ofthe heart rhythm. As illustrated in the right part of FIG. 4D, a lowdegree of regularity (high degree of variability) is observed amongstthe loops in the right part of FIG. 4D.

In FIG. 4E, an index of regularity can be determined with respect to thefirst cardiac signal (analysis signal) against the second (reference)cardiac signal in the first signal pair. An index of variability can bedetermined instead of the index of regularity. In some embodiments, theindex of variability is the inverse of the index of regularity. The highindex of regularity (or low index of variability) indicates anapproximate congruence (e.g., mathematical congruence) of the pluralityof coordinate pairs between the first cardiac signal and the secondcardiac signal. The index of regularity (index of variability) can bedetermined in one of a time domain, frequency domain and spatial domain.Various other methods described herein can also be used. A furtherdetermination can be made as to whether the index of regularity of FIG.4E exceeds a threshold. In some embodiments, the threshold can bedefined to indicate an upper percentile (e.g., top 5^(th) percentile) ofall indexes of regularity of the first (analysis) cardiac signal againstsecond (reference) cardiac signals, i.e., the adjacent eight (8)signals, repeated for signals from 64 or 128 sensor locations. Adifferent percentile can be used, e.g., 10^(th) percentile, or antherpercentile number. Similarly, various percentiles can be defined for thecoefficients of variability, e.g., top 5^(th) or 10^(th) percentile ofvariability.

With reference to the frequency domain, a frequency analysis (e.g.,Fourier analysis) can be performed using a selected parameter associatedwith the plurality of coordinate pairs (or loops) to generate afrequency spectrum, as shown in FIG. 4E. The selected parameter can beamplitude (e.g., voltage), angle, vector, area and derivative.Thereafter, at least one peak is determined in the frequency spectrum ofFIG. 4E. In some embodiments, the at least one peak can include afundamental frequency. In other embodiments, the at least one peak caninclude the fundamental frequency and also one or more harmonics of thefundamental frequency. In still other embodiments, the at least one peakcan include only one or more of the harmonics of the fundamentalfrequency, i.e., the fundamental frequency can be excluded.

In performing the frequency analysis, a sum of the area of the at leastone peak in the frequency spectrum in FIG. 4E can be calculated. Aresult (e.g., index of regularity) can be calculated by dividing the sumof the area of the at least one peak by a total area of the frequencyspectrum within a predefined frequency range, such as between about 4 Hzand about 12 Hz. In some embodiments, other frequency ranges can bedefined. It can be determined whether the result (index of regularity)exceeds the threshold, such as an upper percentile (e.g., top 5^(th)percentile) of all indexes of regularity of the first (analysis) cardiacsignal against second (reference) cardiac signals, i.e., the adjacenteight (8) signals, repeated for signals from 64 or 128 sensor locations.For example, the index of regularity for the coordinate pairs of FIG. 4Cas shown in FIG. 4E is 0.178, indicating a high degree of regularity ofthe first cardiac signal against the second cardiac signal in the firstsignal pair. In frequency analysis, the index of regularity can be in arange between about 0.0 and about 1.0. An index of variability can berepresented as the inverse of the index of regularity, or can becalculated using other computational methods described herein.Similarly, various percentiles can be defined for the index orcoefficient of variability.

In FIG. 4F, an index of regularity is determined with respect to thesecond (analysis) cardiac signal against the third (reference) cardiacsignal in the second signal pair. An index or coefficient of variabilitycan be determined instead of the index of regularity. In someembodiments, the index of variability is the inverse of the index ofregularity. The high index of regularity (or low index of variability)indicates an approximate congruence (e.g., mathematical congruence) ofthe plurality of coordinate pairs between the second cardiac signal andthe third cardiac signal. As described previously, the index ofregularity (index of variability) can be determined in one of a timedomain, frequency domain and spatial domain. Various method describedherein can be used. A further determination can be made as to whetherthe index of regularity of FIG. 4F exceeds a threshold. In someembodiments, the threshold indicates an upper percentile (e.g., top5^(th) percentile) of all indexes of regularity of the second (analysis)cardiac signal against third (reference) cardiac signals, i.e., theadjacent eight (8) signals, repeated for signals from 64 or 128 sensorlocations. A different percentile can be used, e.g., 10^(th) percentile,or anther percentile number. Similarly, various percentiles can also bedefined for the coefficients of variability, e.g., top 5^(th) or 10^(th)percentile of variability.

Moreover, a frequency analysis (e.g., Fourier analysis) can be performedusing a selected parameter associated with the plurality of coordinatepairs to generate a frequency spectrum, as shown in FIG. 4F. Theselected parameter can be amplitude (e.g., voltage), angle, vector, areaand derivative. Thereafter, at least one peak can be determined in thefrequency spectrum of FIG. 4F. In some embodiments, the at least onepeak can include a fundamental frequency. In other embodiments, the atleast one peak can include the fundamental frequency and also one ormore harmonics of the fundamental frequency. In still other embodiments,the at least one peak can include only one or more of the harmonics ofthe fundamental frequency, i.e., the fundamental frequency can beexcluded.

In performing the frequency analysis, a sum of the area of the at leastone peak in the frequency spectrum of FIG. 4F can be calculated. Aresult (i.e., index of regularity) can be calculated by dividing the sumof the area of the at least one peak by a total area of the frequencyspectrum within a predefined frequency range, such as between about 4 Hzand about 12 Hz. In some embodiments, other frequency ranges can bedefined. It can be determined whether the result (index of regularity)exceeds the threshold, e.g., an upper percentile (e.g., top 5^(th)percentile) of all indexes of regularity of the first (analysis) cardiacsignal against second (reference) cardiac signals, i.e., the adjacenteight (8) signals, repeated for signals from 64 or 128 sensor locations.For example, the index of regularity for the coordinate pairs of FIG. 4Das shown in FIG. 4F is 0.073, indicating a low degree of regularity ofthe second cardiac signal against the third cardiac signal. An index ofvariability can be represented as the inverse of the index ofregularity, or can be calculated using other computational methodsdescribed herein. Similarly, various percentiles can be defined for theindex of variability.

As shown in FIG. 4C and FIG. 4E, the first signal pair can have a lowcoefficient of variability (high index of regularity). In other words,the signals in the first signal pair can be regular, e.g., notdisorganized in amplitude (voltage) and time. As shown in FIG. 4D andFIG. 4F, the second signal pair can have a high coefficient ofvariability (low index of regularity). In other words, the signals inthe second signal pair can be irregular or of higher variability thanthe signals in the first signal pair, e.g., disorganized in amplitude(voltage) and/or time (offset). Accordingly, it can be determined thatthe source of complex rhythm disorder lies in the direction of highervariability, from the first pair in the direction of the second signalpair.

FIG. 5 is a flowchart that illustrates an example method 500 ofdetermining periodic repeating activity and/or variability to identify asource of a heart rhythm disorder. The method starts at operation 502.At operations 504, 506, a pair of cardiac signals is selected form aplurality of cardiac signals, representing a signal pair. Specifically,at operation 504 a first (analysis) signal is selected from theplurality of signals and at operation 506 a second different (reference)signal is selected from the plurality of signals. As described herein,there can be 64, 128, or another number of signals. The signals can beECG signals processed via signal processing device 114 of FIG. 1.

At operation 508, a time point is selected with reference to theprocessing of the first cardiac signal with respect to the secondcardiac signal for the selected signal pair. At operation 510, arelational characteristic(s) can be calculated using the time point.This characteristic(s) can be stored, such as in database 118. Thecharacteristic(s) can identify the relationship between the time points.In some embodiments, a derivative of each signal can be determined atthe selected time point. Specifically, a derivative of the first cardiacsignal can be processed against a derivative of the second cardiacsignal at the selected time point to define a coordinate pair of thefirst cardiac signal against the second cardiac signal for the selectedsignal pair.

At operation 512, a determination is made as to whether all time pointshave been processed for the selected signal pair. If it is determinedthat all time point have not been processed, the method continues toperform operations 508-512 until all time points have been processed. Ifit is determined that all time point have been processed, the method 500continues at operation 514.

At operation 514, an index or coefficient of variability (or index ofregularity) is computed between the signals in the selected signal pair,for example, using the relational characteristic(s). Various techniquesdescribed herein can be used, such as standard deviation analysis,frequency analysis, entropy analysis, cross-correlation analysis,randomness analysis, Monte Carlo simulation methods, quantification ofchaos and/or other complex statistical analyses, as well as variouscombinations thereof. At operation 516, it is determined whether alldesired second (reference) signals have been used in relation to theselected first (analysis) signal. If it is determined that all desiredsecond signals have not been used, the method 500 continues atoperations 506-516 until all desired second signals have been used inrelation to the first selected signal. In some embodiments, thecoordinate pairs associated with processing of the first (analysis)cardiac signal against all the second (reference) cardiac signals fordifferent signal pairs at the plurality of time points can be plottedand connected to generate a plurality of loops, as shown in FIGS. 4B and4C. In other embodiments, coefficients of variability (or indexes ofregularity) can be stored as a matrix (e.g., in database 118).

If it is determined that all desired second signals have been processed,the method 500 continues at operation 518 where it is determined whetherall desired first (analysis) signals have been used. If it is determinedthat all desired first signals have not been used, the method 500continues at operations 504-516 until all desired first signals havebeen used.

At operation 520, it is determined whether multiple first and multiplesecond signals were used. If it is determined that multiple signals werenot used, then at operation 522 a coefficient of variability (or anindex of regularity) can be returned for the selected first and secondsignals, i.e., the selected signal pair. However, if it is determinedthat multiple first signals and multiple second signals were used (i.e.,multiple signal pairs), then at operation 524 the matrix of coefficientsof variability (or matrix of indexes of regularity) can be plotted foreach signal pair of first and second signals. (See the plot in FIG. 7).In some embodiments, the coefficients of variability (or indexes ofregularity) can be maintained in memory and/or stored in database 118.The one or more coefficients of variability (indexes of regularity) thatexceed one or more thresholds can be indicated or identified usingdifferent colors as will be described herein in reference to FIGS. 6 and7. As described herein, a threshold can be used to indicate an upperpercentile (e.g., top 5^(th) percentile) of coefficients of variability(or indexes of regularity). The method ends at operation 526.

FIGS. 6A-6E illustrate an example migration of a source (locus) of aheart rhythm disorder and the use of coefficients of variability(indexes of regularity) to identify such a source of heart rhythmdisorder in a patient. Specifically, FIGS. 6A-6E show atrialfibrillation (AF), with termination purely by ablation at the source ofthe AF as identified by the coefficients of variability (indexes ofregularity). In FIG. 6A, left atrial rotational source during AF istraditionally visualized using contours of activation time (e.g.,isochrones). FIG. 6B shows that the AF source moves (precesses) in asmall region shown by the locus of migration in FIG. 6A.

In FIG. 6C, coefficients of variability (indexes of regularity)illustrate a region of high variability or low regularity (cool colors,indicated by an arrow) relative to and surrounded by adjacent regions oflow variability or high regularity (warmer colors). This region is asource of the AF and agrees precisely with the rotational source in FIG.6A. As shown on patient specific geometry in FIG. 6C, the source for AFis in the low left atrium. In FIG. 6D, electrode signals are shownduring AF with termination to sinus rhythm by <1 minute after ablationat the region of high variability or low regularity surrounded by aregion of low variability or high regularity (i.e., rotational source inFIG. 6A) (ECG lead aVF, and electrodes at ablation catheter, coronarysinus). In FIG. 6E, an isochronal map of the sinus rhythm is shown forthe referenced patient. This patient remains free of AF on implantedcardiac monitor. (Scale Bar=1 cm).

FIG. 7 illustrates an example plot of indexes of regularity to identifya source of heart rhythm disorder.

The example plot of coefficients of variability (indexes of regularity)for each signal processed can be generated as a grid of sub-plots, witheach sub-plot showing the coefficient of variability (index ofregularity) using a different first (analysis) signal and every second(reference) signal processed against the first signal. Thereafter, theexample plot can be generated as a combination of sub-plots. The exampleplot arranges each signal in an approximate spatial relationship withthe other signals. A color is assigned to a pixel at each sensorlocation representing a value of the coefficient of variability (indexof regularity) for the signal pair. For example, lower coefficient ofvariability (higher index of regularity) can be coded in red colors,while higher coefficient of variability (lower index of regularity) canbe coded in blue colors. Each first (analysis) signal's sub-plot canthen be placed into the larger example plot that represents that firstsignal's spatial location with the other processed first (analysis)signals, creating an 8×8 plot as shown in FIG. 7.

As shown in FIG. 7, regions of low variability or high regularity (warmcolors) that surround a central region of high variability or lowregularity (cool colors) can be determined. The black arrow indicates asite of successful ablation. Thus, a region of high variability (lowregularity) that is surrounded by regions of low variability (highregularity) can be determined for ablation to eliminate the source ofthe cardiac rhythm disorder.

FIG. 8 is a block diagram of an illustrative embodiment of a generalcomputer system 800. The computer system 800 can be the signalprocessing device 114 and the computing device 116 of FIG. 1. Thecomputer system 800 can include a set of instructions that can beexecuted to cause the computer system 800 to perform any one or more ofthe methods or computer based functions disclosed herein. The computersystem 800, or any portion thereof, may operate as a standalone deviceor may be connected, e.g., using a network or other connection, to othercomputer systems or peripheral devices. For example, the computer system800 may be operatively connected to signal processing device 114 andanalysis database 118.

In operation as described with reference to FIGS. 1-7, theidentification of source(s) of heart rhythm disorders as describedherein can be used to identify patients in whom therapy can be effectiveand to assist in guiding such therapy, which can include delivery of oneor more of ablation, electrical energy, mechanical energy, drugs, cells,genes and biological agents to at least a portion of the identifiedsource(s) of the heart.

The computer system 800 may also be implemented as or incorporated intovarious devices, such as a personal computer (PC), a tablet PC, apersonal digital assistant (PDA), a mobile device, a palmtop computer, alaptop computer, a desktop computer, a communications device, a controlsystem, a web appliance, or any other machine capable of executing a setof instructions (sequentially or otherwise) that specify actions to betaken by that machine. Further, while a single computer system 800 isillustrated, the term “system” shall also be taken to include anycollection of systems or sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

As illustrated in FIG. 8, the computer system 800 may include aprocessor 802, e.g., a central processing unit (CPU), agraphics-processing unit (GPU), or both. Moreover, the computer system800 may include a main memory 804 and a static memory 806 that cancommunicate with each other via a bus 826. As shown, the computer system800 may further include a video display unit 810, such as a liquidcrystal display (LCD), an organic light emitting diode (OLED), a flatpanel display, a solid state display, or a cathode ray tube (CRT).Additionally, the computer system 800 may include an input device 812,such as a keyboard, and a cursor control device 814, such as a mouse.The computer system 800 can also include a disk drive unit 816, a signalgeneration device 822, such as a speaker or remote control, and anetwork interface device 808.

In a particular embodiment, as depicted in FIG. 8, the disk drive unit816 may include a computer-readable medium 818 in which one or more setsof instructions 820, e.g., software, can be embedded. Further, theinstructions 820 may embody one or more of the methods or logic asdescribed herein. In a particular embodiment, the instructions 820 mayreside completely, or at least partially, within the main memory 804,the static memory 806, and/or within the processor 802 during executionby the computer system 800. The main memory 804 and the processor 802also may include computer-readable media.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments, the methods described herein maybe implemented by software programs tangibly embodied in aprocessor-readable medium and may be executed by a processor. Further,in an exemplary, non-limited embodiment, implementations can includedistributed processing, component/object distributed processing, andparallel processing. Alternatively, virtual computer system processingcan be constructed to implement one or more of the methods orfunctionality as described herein.

It is also contemplated that a computer-readable medium includesinstructions 820 or receives and executes instructions 820 responsive toa propagated signal, so that a device connected to a network 824 cancommunicate voice, video or data over the network 824.

Further, the instructions 820 may be transmitted or received over thenetwork 824 via the network interface device 808.

While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, example embodiment, the computer-readablemedium can include a solid-state memory, such as a memory card or otherpackage, which houses one or more non-volatile read-only memories.Further, the computer-readable medium can be a random access memory orother volatile re-writable memory. Additionally, the computer-readablemedium can include a magneto-optical or optical medium, such as a diskor tapes or other storage device to capture carrier wave signals, suchas a signal communicated over a transmission medium. A digital fileattachment to an e-mail or other self-contained information archive orset of archives may be considered a distribution medium that isequivalent to a tangible storage medium. Accordingly, any one or more ofa computer-readable medium or a distribution medium and otherequivalents and successor media, in which data or instructions may bestored, are included herein.

In accordance with various embodiments, the methods described herein maybe implemented as one or more software programs running on a computerprocessor. Dedicated hardware implementations including, but not limitedto, application specific integrated circuits, programmable logic arrays,and other hardware devices can likewise be constructed to implement themethods described herein. Furthermore, alternative softwareimplementations including, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

It should also be noted that software that implements the disclosedmethods may optionally be stored on a tangible storage medium, such as:a magnetic medium, such as a disk or tape; a magneto-optical or opticalmedium, such as a disk; or a solid state medium, such as a memory cardor other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories. The software may also utilize a signal containing computerinstructions. A digital file attachment to e-mail or otherself-contained information archive or set of archives is considered adistribution medium equivalent to a tangible storage medium.Accordingly, a tangible storage medium or distribution medium as listedherein, and other equivalents and successor media, in which the softwareimplementations herein may be stored, are included herein.

Thus, a system and method to identify a source (or sources) of abiological rhythm disorder have been described. Although specificexample embodiments have been described, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be utilized and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of any of the above-described embodiments, and otherembodiments not specifically described herein, may be used and are fullycontemplated herein.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate example embodiment.

The invention claimed is:
 1. A method of locating a source of a rhythmdisorder of a heart, the method comprising: processing a first pair ofcardiac signals to define a first coefficient associated withvariability among the first pair of signals at a first region of theheart; processing a second pair of cardiac signals to define a secondcoefficient associated with variability among the second pair of signalsat a second region of the heart; and comparing the first coefficient ofvariability to the second coefficient of variability to determine adirection towards the source of the rhythm disorder; wherein variabilityis determined independent of activation onsets within the signals. 2.The method of claim 1, wherein processing the first pair of cardiacsignals comprises processing a first cardiac signal at one or more firsttime points against a second cardiac signal at one or more second timepoints to define the first coefficient of variability based on one ormore coordinate pairs of the first cardiac signal against the secondcardiac signal.
 3. The method of claim 1, wherein processing the secondpair of cardiac signals comprises processing a third cardiac signal atone or more third time points against a fourth cardiac signal at one ormore fourth time points to define the second coefficient of variabilitybased on one or more coordinate pairs of the third cardiac signalagainst the fourth cardiac signal.
 4. The method of claim 1, wherein thefirst cardiac signal and the third cardiac signal comprise a commonsignal.
 5. The method of claim 1, wherein the first and second regionsare the same region of the heart.
 6. The method of claim 1, wherein thefirst and second regions are different regions of the heart.
 7. Themethod of claim 1, wherein at least one of the first coefficient ofvariability and the second coefficient of variability is determined fromat least one of a variability in signal timing and a variability insignal amplitude among at least one of the first pair of cardiac signalsand the second pair of cardiac signals.
 8. The method of claim 1,wherein at least one of the first coefficient of variability and thesecond coefficient of variability is determined from variability insignal shape among at least one of the first pair of cardiac signals andthe second pair of cardiac signals.
 9. The method of claim 1, wherein atleast one of the first coefficient of variability and the secondcoefficient of variability is determined by one or more methods selectedfrom a group consisting of standard deviation analysis, frequencyanalysis, entropy analysis, cross-correlation analysis, randomnessanalysis, Monte Carlo simulation methods, quantification of chaos, othercomplex statistical analyses, and combinations thereof.
 10. The methodof claim 1, wherein the variability is in one or more of amplitude,voltage, motion, direction, impedance, conductance, and anotherdimension other than time.
 11. The method of claim 1, wherein thevariability is in time.
 12. The method of claim 1, wherein the methodfurther comprises: iteratively selecting the first pair of cardiacsignals and the second pair of cardiac signals from a plurality ofcardiac signals, each iteratively selected pair differing in at leastone cardiac signal; processing the first iteratively selected signal andthe second iteratively selected signal from each pair for each iterationto define the first coefficient of variability and the secondcoefficient of variability, respectively; constructing a matrix ofcoefficients associated with variability for the iteratively selectedpairs of cardiac signals; and determining one or more sources of therhythm disorder using the matrix of coefficients.
 13. The method ofclaim 12, wherein determining the one or more sources comprisesidentifying from the matrix one or more regions of the heart associatedwith higher coefficients of variability surrounded by regions of lowercoefficients of variability.
 14. The method of claim 1, wherein thesource of the rhythm disorder is located in a direction from lowercoefficients of variability towards higher coefficients of variability.15. A system to locate a source of a rhythm disorder of a heart, thesystem comprising at least one computing device configured to: process afirst pair of cardiac signals to define a first coefficient associatedwith variability among the first pair of signals at a first region ofthe heart; process a second pair of cardiac signals to define a secondcoefficient associated with variability among the second pair of signalsat a second region of the heart; and compare the first coefficient ofvariability to the second coefficient of variability to determine adirection towards the source of the rhythm disorder; wherein variabilityis determined independent of activation onsets within the signals. 16.The system of claim 15, wherein the at least one computing device isconfigured to processes a first cardiac signal at one or more first timepoints against a second cardiac signal at one or more second time pointsto define the first coefficient of variability based on one or morecoordinate pairs of the first cardiac signal against the second cardiacsignal.
 17. The system of claim 15, wherein the at least one computingdevice is configured to processes a third cardiac signal at one or morethird time points against a fourth cardiac signal at one or more fourthtime points to define the second coefficient of variability based on oneor more coordinate pairs of the third cardiac signal against the fourthcardiac signal.
 18. The system of claim 15, wherein the first cardiacsignal and the third cardiac signal comprise a common signal.
 19. Thesystem of claim 15, wherein the first and second regions are the sameregion of the heart.
 20. The system of claim 15, wherein the first andsecond regions are different regions of the heart.
 21. The system ofclaim 15, wherein at least one of the first coefficient of variabilityand the second coefficient of variability is determined from at leastone of variability in signal timing and variability in signal amplitudeamong at least one of the first pair of cardiac signals and the secondpair of cardiac signals.
 22. The system of claim 15, wherein at leastone of the first coefficient of variability and the second coefficientof variability is determined from variability in signal shape among atleast one of the first pair of cardiac signals and the second pair ofcardiac signals.
 23. The system of claim 15, wherein at least one of thefirst coefficient of variability and the second coefficient ofvariability is determined by one or more methods selected from a groupconsisting of standard deviation analysis, frequency analysis, entropyanalysis, cross-correlation analysis, randomness analysis, Monte Carlosimulation methods, quantification of chaos, other complex statisticalanalyses, and combinations thereof.
 24. The system of claim 15, whereinthe variability is in one or more of amplitude, voltage, motion,direction, impedance, conductance, and another dimension other thantime.
 25. The system of claim 15, wherein the variability is in time.26. The system of claim 15, wherein the at least one computing device isconfigured to: iteratively select the first pair of cardiac signals andthe second pair of cardiac signals from a plurality of cardiac signals,each iteratively selected pair differing in at least one cardiac signal;process the first iteratively selected signal and the second iterativelyselected signal from each pair for each iteration to define the firstcoefficient of variability and the second coefficient of variability,respectively; construct a matrix of coefficients associated withvariability for the iteratively selected pairs of cardiac signals; anddetermine one or more sources of the rhythm disorder using the matrix ofcoefficients.
 27. The system of claim 15, wherein the at least onecomputing device is further configured to identify from the matrix oneor more regions of the heart associated with higher coefficients ofvariability surrounded by regions of lower coefficients of variabilityto determine one or more sources of the rhythm disorder.
 28. The systemof claim 15, wherein the source of the rhythm disorder is located in adirection from lower coefficients of variability towards highercoefficients of variability.